Generating a representation of high-frequency electric power delivery system data using deviations from a trend

ABSTRACT

A system, method, and computer program product are provided for representation of high-frequency signal data. In use, input data is received including high-frequency signals, wherein the input data is of a first width. Next, the input data is processed to manage display of the input data, where specifically the input data is divided into one or more segments based on first criteria including the first width, and from each segment of the one or more segments, a maximum value is identified and a minimum value is identified. The maximum and minimum may be trend maximum and minimum values. The input data is transformed to a visualizable representation of the high-frequency signals, the visualizable representation of the high-frequency signals including a plot of the maximum value and the minimum value for each segment of the one or more segments. Additionally, the plot is displayed.

PRIORITY APPLICATION

This application claims benefit as a Continuation-in-Part of U.S.Non-Provisional application Ser. No. 15/727,012 filed on 6 Oct. 2017,naming Eric J. Hewitt and Matthew J. Halladay as inventors; which isincorporated by reference herein in its entirety.

FIELD OF THE INVENTION

The present invention relates to presenting signal data, and moreparticularly to improving the representation of high-frequency signaldata.

BACKGROUND

Currently, analytical data gathered from an electrical powerdistribution system may be used to understand power generation anddiagnose potential problems. Often, such analytical data may includehigh-frequency data. Displaying high-frequency data, however, may pose avariety of issues, including slow processing time, inaccuraterepresentation of the displayed data, etc. Additionally, such issues areoften increased as the data is zoomed out. Current methods to rectifysuch issues include, for example, a filter applied to the analyticaldata to remove the high frequency. Such an approach, however, fails toallow for analysis of the high-frequency data. A second approach mayinclude rendering the high-frequency data by applying an alias,resulting in the high-frequency data being displayed as alower-frequency signal. However, this second approach may cause aninaccurate representation of the high-frequency data.

There is thus a need for addressing these and/or other issues associatedwith the prior art.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a method for generating a representation ofhigh-frequency signal data, in accordance with one embodiment.

FIG. 2 illustrates a diagram of an electric power delivery system, inaccordance with one embodiment.

FIG. 3 illustrates a method for generating a representation ofhigh-frequency signal data, in accordance with one embodiment.

FIG. 4A illustrates a plot showing exemplary data with an unfiltered 60Hz fundamental signal at zoom level 100%, S value of 512, and T value of512, in accordance with one embodiment.

FIG. 4B illustrates a plot showing exemplary data with a high-passfiltered 60 Hz fundamental signal at zoom level 100%, S value of 512,and T value of 512, in accordance with one embodiment.

FIG. 5A illustrates a plot showing exemplary data with an unfiltered 60Hz fundamental signal at zoom level 10%, S value of 5000, and T value of512, in accordance with one embodiment.

FIG. 5B illustrates a plot showing exemplary data with a high-passfiltered 60 Hz fundamental signal at zoom level 10%, S value of 5000,and T value of 512, in accordance with one embodiment.

FIG. 6A illustrates a plot showing exemplary data with an unfiltered 60Hz fundamental signal at zoom level 1%, S value of 50000, and T value of512, in accordance with one embodiment.

FIG. 6B illustrates a plot showing exemplary data with a high-passfiltered 60 Hz fundamental signal at zoom level 1%, S value of 50000,and T value of 512, in accordance with one embodiment.

FIG. 7A illustrates a plot showing exemplary data with an unfiltered 60Hz fundamental signal at zoom level 0.1%, S value of 500000, and T valueof 512, in accordance with one embodiment.

FIG. 7B illustrates a plot showing exemplary data with a high-passfiltered 60 Hz fundamental signal at zoom level 0.1%, S value of 500000,and T value of 512, in accordance with one embodiment.

FIG. 8A illustrates a plot showing exemplary data with an unfiltered 60Hz fundamental signal at zoom level 0.01%, S value of 5000000, and Tvalue of 512, in accordance with one embodiment.

FIG. 8B illustrates a plot showing exemplary data with a high-passfiltered 60 Hz fundamental signal at zoom level 0.01%, S value of5000000, and T value of 512, in accordance with one embodiment.

FIG. 9 illustrates a method for transferring information for generatinga representation of high-frequency signal data, in accordance with oneembodiment.

FIG. 10A illustrates a process for identifying min and max downsampledvalues, in accordance with one embodiment.

FIG. 10B illustrates a plot applying the process of FIG. 10A, inaccordance with one embodiment.

FIG. 10C illustrates a plot which is a 10× downsampling of the plotshown in FIG. 10B, in accordance with one embodiment.

FIG. 10D illustrates a plot which is a 100× downsampling of the plotshown in FIG. 10B, in accordance with one embodiment.

FIG. 10E illustrates a plot of a voltage signal, in accordance with oneembodiment.

FIG. 10F illustrates a plot of a 10× min max downsampled signal of theplot shown in FIG. 10E, in accordance with one embodiment.

FIG. 10G illustrates a plot of a 100× min max downsampled signal of theplot shown in FIG. 10E, in accordance with one embodiment.

FIG. 10H illustrates a plot of a 1000× min max downsampled signal of theplot shown in FIG. 10E, in accordance with one embodiment.

FIG. 10I illustrates a plot of a 10000× min max downsampled signal ofthe plot shown in FIG. 10E, in accordance with one embodiment.

FIG. 10J illustrates a plot of a 20000× min max downsampled signal ofthe plot shown in FIG. 10E, in accordance with one embodiment.

FIG. 10K illustrates a plot of a high pass filter applied to a voltagesignal, in accordance with one embodiment.

FIG. 10L illustrates a plot of a 10× min max downsampled signal of theplot shown in FIG. 10K, in accordance with one embodiment.

FIG. 10M illustrates a plot of a 100× min max downsampled signal of theplot shown in FIG. 10K, in accordance with one embodiment.

FIG. 10N illustrates a plot of a 1000× min max downsampled signal of theplot shown in FIG. 10K, in accordance with one embodiment.

FIG. 11 illustrates a network architecture, in accordance with onepossible embodiment.

FIG. 12 illustrates an exemplary system, in accordance with oneembodiment.

FIGS. 13A and 13B illustrate plots of a power system signal over timewith min max values (FIG. 13A) and interpolated min max values (FIG.13B).

FIGS. 14A and 14B illustrate plots of a power system signal over timewith min max values (FIG. 14A) and interpolated min max values (FIG.14B).

FIG. 15 illustrates a process for using min max or interpolated min maxin accordance with several embodiments herein.

DETAILED DESCRIPTION

FIG. 1 illustrates a method 100 for generating a representation ofhigh-frequency signal data, in accordance with one embodiment. As shown,in operation 102, input data is received including high-frequencysignals, wherein the input data is of a first width. In the context ofthe present description, the aforementioned input data may include anyset of signals (including potentially high-frequency signals) generatedby a power distribution system. For example, such signals may begenerated by faults, partial discharge activity, equipment operation(e.g., breakers operating, capacitor banks switching, voltage tapchanges, etc.).

Still yet, in the present description, a high-frequency signal may referto any frequency considered high based on the context from which thesignal is obtained. For example, a high-frequency signal relating tospeech may be 3 kHz, whereas a high-frequency signal relating to awireless transmission may be 5 GHz. Even still, a high-frequency signalrelating to electric power systems may be in the range of 1 kHZ.

In the present description, the first width may refer to any measurementfrom side to side of the signals included in the input data. Forexample, in one possible embodiment, the width may be what is displayedwithin a window, or may be based on a size of a window in which thesignals are displayed.

The input data is then processed in operation 104 to manage display ofthe input data, including: dividing the input data into one or moresegments based on first criteria including the first width; identifying,from each segment of the one or more segments, a maximum value; andidentifying, from each segment of the one or more segments, a minimumvalue. The first width may be determined based, at least in part, on thefirst criteria. In various embodiments, the first criteria may includeat least one of a pixel resolution of a window, a zoom factor (or aplurality of zoom factors), a width associated with a maximum value, awidth associated with a minimum value, or an error factor associatedwith the input data. Also, in the context of the present description,the first criteria may include the error factor that is an identifiedsignal disturbance. Further, the first criteria may include presetoptimal settings with a preconfigured zoom level. Specifically, suchoptimal settings may include pre-viewing actions and current-viewingactions. Pre-viewing actions may include applying various settings (e.g.zoom level 1, zoom level 2, etc.) to the input data such that resultsare generated before they are requested or viewed. Current-viewingactions may include applying various settings (e.g. window size X, zoomlevel Y, minimum value width Z, etc.) to be displayed. In oneembodiment, such current-viewing actions may be applied when a userinterface (e.g. showing the plot, as will be elaborated upon later ingreater detail) is initially requested, or may be applied as manuallyrequested (e.g. based on a default setting or preconfigured setting tobe applied as desired, etc.).

Thus, the first criteria may be used to modify a display (used topresent the representation of the input data), configure predeterminedsettings, and/or personalize the interaction with the plot in somemanner. In one embodiment, the width of the segment of the one or moresegments may be based on one of natural numbers or real numbers.Further, a pre-identification of a pre-maximum value and a pre-minimumvalue may be identified for use with the first criteria.

In the context of the present description, the one or more segments mayinclude any grouping of the input data. For example, the input data(i.e. signals) may be divided into a number of bins (e.g. frequencybins) equal to the width of an output (e.g. minimum value or maximumvalue). In one embodiment, the width of a segment of the one or moresegments may be equal to or less than the first width of the input data.

In the context of the present description, the maximum value maycorrespond with a maximum point of a segment of the input data (e.g.frequency signal) and the minimum value may correspond with a minimumpoint of a segment of the input data (e.g. frequency signal). In oneembodiment, the first criteria may include a width of a segment of theone or more segments, wherein the width of the segment is based on atime interval associated with the maximum value or the minimum value.Also, in the context of the present description, each of the maximumvalue and the minimum value may be recorded as a vector. Further, theinput data may be filtered prior to identifying the maximum value or theminimum value.

In one embodiment, receiving the input data may include receiving amaximum value input and a minimum value input, where the maximum valueinput may be used to identify the maximum value, and the minimum valueinput may be used to identify the minimum value. As an option, a widthof the maximum value input may differ from a width of the maximum value,and a width of the minimum value input may differ from a width of theminimum value.

With continuing reference to FIG. 1, in operation 106, the input data istransformed to a visualizable representation of the high-frequencysignals, the visualizable representation of the high-frequency signalsincluding a plot of the maximum value and the minimum value for eachsegment of the one or more segments. In the context of the presentdescription, transforming the input data to a visuablizablerepresentation may include receiving the raw input data, extractingpoints of interest (e.g. maximum value, minimum value, etc.) and thenconstructing a representation of the input data based on such points ofinterest. In this manner, only a fraction of the raw input data (in theform of the maximum value and the minimum value) may be used toconstruct the representation.

To this end, in operation 108, a plot may be displayed. In oneembodiment, the plot may include a shaded region between the maximumvalue and the minimum value for each segment of the one or moresegments. Additionally, an overlay may be displayed over the plot. Forexample, the overlay may include one of a median value of each segmentof the one or more segments or a filtered sampled value, the overlaybeing displayed in a different color than a color of the shaded region.

The plot may present the identified signal disturbance. Of course, it isappreciated that any signal deviation or anomaly may be additionallydisplayed.

Still yet, the plot may be saved. To this end, the saved plot mayrequire less data (in comparison to the input data) and may preserve rawdata (e.g. maximum value, minimum value, etc.) of the input data.Additionally, the plot may be saved with raw data based on the inputdata, wherein the raw data may include, at a minimum, the maximum valueand the minimum value. Further, the plot may be sent to a downstreamdevice in an efficient manner (via reduced data for transmission anddisplay).

FIG. 2 illustrates a diagram of an electric power delivery system 200,in accordance with one embodiment. As an option, the system 200 may beimplemented in the context of any one or more of the embodiments setforth in any previous and/or subsequent figure(s) and/or descriptionthereof. However, it is to be appreciated that the system 200 may beimplemented in the context of any desired environment. In particular,the system 200 may provide one example where high-frequency signal datamay be obtained, as described in relation to method 100.

As shown, system 200 includes a simplified example of an electric powerdelivery system. Such a system 200 may include generator 230 which maybe monitored by an intelligent electronic device (IED) (e.g. IED 164),and may generate electric power. Generator 230 may be connected to bus219 via a circuit breaker 211, which may be controlled using IED 266,and via transformer 214 (which may be a step-up transformer fortransforming the voltage of the electric power from generator 230 to ahigher voltage suitable for an electric power transmission system).

Bus 219 and bus 223 may be connected via transmission lines 220 a and220 b operating at transmission-level voltages. Transmission line 220 bmay include circuit breakers 245 and 251 monitored and controlled byIEDs 269 and 254. Transmission line 220 a may include circuit breakers244 and 250 monitored and controlled by IEDs 256 and 260. Transmissionline 220 b may be monitored by an IED 252 which may be capable ofproviding differential protection. Similarly, transmission line 220 amay be monitored and controlled by IED 258 which may be capable ofproviding differential protection. Alternatively, transmission lines 220a and 220 b may be monitored and/or controlled using multiple IEDs suchas one IED at each end of the transmission lines capable of performingdifferential protection using communication therebetween.

Bus 223 may be connected to a distribution bus 225 using a step-downtransformer 224 that may be capable of stepping down the voltage fromthe transmission bus 223 to distribution levels. Bus 225 may bemonitored and/or controlled using IED 268, and may provide electricpower to load 295 (where the voltage may be stepped further down using atransformer). A further transformer 292 and circuit breaker 291 mayconnect transmission bus 225 to generator 290. Circuit breaker 291 maybe monitored and controlled via IED 293. Generator 290 may be adistributed generator such as a solar-power generator, wind turbine,natural gas electric generator, diesel generator, or the like.

Although not separately illustrated, the various IEDs may obtainelectric power information from the monitored equipment in system 200using potential transformers (PTs, for voltage measurements), currenttransformers (CTs, for current measurements), etc. The PTs and CTs mayinclude any device capable of providing outputs that can be used by theIEDs to make potential and current measurements, and may includetraditional PTs and CTs, optical PTs and CTs, Rogowski coils,hall-effect sensors, etc. Furthermore, although not separatelyillustrated, each IED may include access to a common time source. Thecommon time source may be distributed via a communications network(using, for example, IEEE-1588 protocol, NTP protocol, or the like), orobtained locally at each IED. The common time source may be a universaltime, such as that delivered using global positioning system (GPS)satellites, WWVB, WWV, etc. A common time may be used totime-synchronize measurements of the electric power system, and in thecalculation of synchrophasors. Measurements may be paired with a timestamp or time tag indicating a time at which the measurement was made.Accordingly, phasors calculated by the IEDs may include a time stampindicating a time at which the measurement was made.

IEDs in system 200 may be configured to communicate phasor and/orsynchrophasor information to a central unit such as Phasor DataConcentrator (PDC) 270. The PDC 270 may be capable of receiving andstoring the phasor and/or synchrophasor or other gathered or calculatedpower system information (hereinafter “PMU data”). The PDC 270 may be incommunication with a mass storage device 284 capable of storing the PMUdata received by PDC 270.

PDC 270 may also be in communication with a number of other devices orsystems that may consume PMU data. Such devices or systems may include,for example, a Wide Area Control and Situational Awareness (WCSA) System280, Supervisory Control and Data Acquisition (SCADA) System 282, localHuman-Machine Interface (HMI) 286, or automation controller 272. PDC 270may further include a time input, which may receive a time signal from acommon time source 288.

In relation to method 100 specifically, one or more elements in system200 may be used a basis for input data (e.g. signal data). For example,one or more IEDs (e.g. 252, 254, 256, 258, 260, 264, 266, 268, 269, 293,etc.) may be used to collect or gather information relating to signaldata of the power distribution system. Such IEDs may collect the signaldata from any component (e.g. conductor, transformer, converter, bus,etc.) of the power distribution system. Additionally, in one embodiment,a database associated with the one or more IEDs may be physicallyseparated or located remotely from the one or more IEDs.

More illustrative information will now be set forth regarding variousoptional architectures and uses in which the foregoing method may or maynot be implemented, per the desires of the user. It should be stronglynoted that the following information is set forth for illustrativepurposes and should not be construed as limiting in any manner. Any ofthe following features may be optionally incorporated with or withoutthe exclusion of other features described.

FIG. 3 illustrates a method 300 for generating a representation ofhigh-frequency signal data, in accordance with one embodiment. As anoption, the method 300 may be implemented in the context of any one ormore of the embodiments set forth in any previous and/or subsequentfigure(s) and/or description thereof. Of course, however, the method 300may be implemented in the context of any desired environment. Further,the aforementioned definitions may equally apply to the descriptionbelow.

In one embodiment, method 300 may represent one exemplary embodiment forgenerating a representation of high-frequency signal data. Inparticular, method 300 may represent one exemplary algorithm used togenerate a representation of high-frequency signal data. As shown, inoperation 302, a set of input data x[k] of length S is received. In oneembodiment, the length S may correspond with the first width (seeoperation 102). Next, in operations 304 and 306, two sets of output dataare defined, where operation 304 sets a first output data as x_min[k]with length T, and operation 306 sets a second output data as x_max[k]with length T. In method 300, operations 302, 304, and 306 may representinference rules (i.e. given clauses, etc.) used in the algorithm forvisualizing high-frequency signal data.

Per operation 308, input data x[k] is divided into T bins of S/T width.In the context of the present description, a bin may include a segmentof a grouping of the input data. In one embodiment, the number of binsmay equal the length of each output (e.g. x_min[k], x_max[k], etc.). Tothat end, per decision 310, it is determined whether each bin of the Tbins satisfies 0<=K<T. In one embodiment, T may be the width of aminimum value or maximum value. Further, a quantity of samples in eachbin of a plurality of bins may equal the first width divided by a numberof the one or more bins. For example, if length S was 24, and length Twas 8, that would correspond with each bin having three (3) items (i.e.T bins of S/T width). Thus, in one embodiment, if a bin includes three(3) items, then one item would be selected from the bin for x_min[k],another one item would be selected from the bin for x_max[k], and theremaining one item would not be used. While the subject matter is beingdescribed in the foregoing context, it is not meant to be limiting asthose of skill in the art will appreciate that various of the acts andoperations (including different sizes of length S and length T)described hereinafter may also be implemented. Still yet, length S maybe greater or equal to length T, and the result for T bins may include Titems in each output (e.g. per T bins of S/T width).

Additionally, in the context of the present description, whendownsampling data, T may represent the amount of underlying raw datathat is available for a given requested range, and S may represent thenumber of data points that will be used to represent the underlying datato the user or application. As an example, time series data may besampled at 20 thousand points per second, and a user or application mayrequest 13 seconds worth of data to be represented with 500 samples. Insuch an example, T may equal 20000×13=160000 (underlying raw data), andS may equal 500 (number of data points). Each bin may be 320 sampleswide and may result in a single Min and single Max per bin. In oneembodiment, each bin may contain (but is not required to contain) aninteger number of points.

If decision 310 results in a “no”, the method returns to operation 304.Conversely, if decision 310 results in a “yes”, the method proceeds onto operation 312 where, for each bin, the maximum value x_max[k] and theminimum value x_min[k] are identified. In one embodiment, the x_max[k]and x_min[k] may be calculated (see further discussion below relating toFIG. 10A). Additionally, the x[k] data may be filtered prior tocalculating the x_min[k] and x_max[k]. For each bin, per operation 314,the maximum value x_max[k] and the minimum value x_min[k] are stored.For example, the maximum value x_max[k] and the minimum value x_min[k]may be stored in a database attached directly to an IED (or other devicecollecting signal data). In an alternative arrangement, the database maybe physically separated or located remotely (e.g. via the cloud).Additionally, each of the maximum value x_max[k] and the minimum valuex_min[k] may be stored without time limitations (without end), or with apredetermined time constraint (e.g. 30 days, etc.).

Still yet, per operation 316, for each bin, the x_min[k] and thex_max[k] are plotted. In one embodiment, multiple plots may be displayedsimultaneously (i.e. if more than one bin is plotted on a single chart,several x_min[k] points and several x_max[k] points may be plotted).Additionally, where multiple plots are displayed, each bin may operateindependent of any of the other displayed bins. For example, if inputdata associated with a first displayed bin is updated, the plotassociated with the first displayed bin may be updated as well, whereinsuch updates are independent of any other displayed bins.

Lastly, the area between each plotted x_min[k] and x_max[k] is shadedper operation 318. In one embodiment, the shading may be of the samecolor as the plotted x_min[k] value and the plotted x_max[k] value.Conversely, the shading may be of a different color or pattern from theplotted x_min[k] value and the plotted x_max[k] value.

In this manner, input signal data may be downsampled by computing aminimum and maximum value for each downsample window, and a chart may beconstructed showing the minimum value plot and the maximum value plot.

FIG. 4A illustrates a plot 400 showing exemplary data with an unfiltered60 Hz fundamental signal at zoom level 100%, S value of 512, and T valueof 512, in accordance with one embodiment. As an option, the plot 400may be implemented in the context of any one or more of the embodimentsset forth in any previous and/or subsequent figure(s) and/or descriptionthereof. However, it is to be appreciated that the plot 400 may beimplemented in the context of any desired environment.

As shown in plot 400, at zoom level 100%, a disturbance 402 may bereadily observed. Note that the disturbance 402 corresponds withhigh-frequency data. Additionally, at a zoom level of 100%, note that noshading occurs because all of the displayed data points correspond withall of the raw input data. This is verified by a S value of 512, and a Tvalue of 512, which would correspond with only 1 bin. As such, because amaximum value and a minimum value have not been determined (based on thevalues of S and T), shading between a maximum value and a minimum valueis not found.

FIG. 4B illustrates a plot 404 showing exemplary data with a high-passfiltered 60 Hz fundamental signal at zoom level 100%, S value of 512,and T value of 512, in accordance with one embodiment. As an option, theplot 404 may be implemented in the context of any one or more of theembodiments set forth in any previous and/or subsequent figure(s) and/ordescription thereof. However, it is to be appreciated that the plot 404may be implemented in the context of any desired environment.

Similar to plot 400, plot 404 is shown at zoom level 100% but, incomparison to plot 400 which showed an unfiltered signal, plot 404 showsa high-pass filtered signal. Note that the disturbance 406 correspondwith high-frequency data. Additionally, as described hereinabove inrelation to plot 400, no shading is shown for plot 404 because all ofthe displayed data points correspond with all of the raw input data.

FIG. 5A illustrates a plot 500 showing exemplary data with an unfiltered60 Hz fundamental signal at zoom level 10%, S value of 5000, and T valueof 512, in accordance with one embodiment. As an option, the plot 500may be implemented in the context of any one or more of the embodimentsset forth in any previous and/or subsequent figure(s) and/or descriptionthereof. However, it is to be appreciated that the plot 500 may beimplemented in the context of any desired environment.

As shown in plot 500, at zoom level 10%, a disturbance 502 may bereadily observed. Note that the disturbance 502 corresponds withhigh-frequency data. Additionally, at a zoom level of 10%, the inputdata is separated into bins, a maximum value is identified, a minimumvalue is identified, and shading between the maximum value and theminimum value is also shown.

FIG. 5B illustrates a plot 504 showing exemplary data with a high-passfiltered 60 Hz fundamental signal at zoom level 10%, S value of 5000,and T value of 512, in accordance with one embodiment. As an option, theplot 504 may be implemented in the context of any one or more of theembodiments set forth in any previous and/or subsequent figure(s) and/ordescription thereof. However, it is to be appreciated that the plot 504may be implemented in the context of any desired environment.

Similar to plot 500, plot 504 is shown at zoom level 10% but, incomparison to plot 500 which showed an unfiltered signal, plot 504 showsa high-pass filtered signal. Note that the disturbance 506 correspondwith high-frequency data. Additionally, as described hereinabove inrelation to plot 500, shading is shown (and in fact more easily observedthan in plot 500) between the identified maximum value and theidentified minimum value.

FIG. 6A illustrates a plot 600 showing exemplary data with an unfiltered60 Hz fundamental signal at zoom level 1%, S value of 50000, and T valueof 512, in accordance with one embodiment. As an option, the plot 600may be implemented in the context of any one or more of the embodimentsset forth in any previous and/or subsequent figure(s) and/or descriptionthereof. However, it is to be appreciated that the plot 600 may beimplemented in the context of any desired environment.

As shown in plot 600, at zoom level 1%, a slight disturbance 602 may beobserved. Such disturbance 602 is not readily or easily observed for theunfiltered fundamental signal. However, plot 604 (described below) moreeasily displays the disturbance.

As an example, disturbances may be more visible in plot 604 (a min maxdownsampled chart) because of a time dilation of the min max data. Inother words, a point of disturbance may be maintained duringdownsampling, but if the point of disturbance lasts for only 10 samples,and the data is downsampled by a factor of 1000, then the smallesttemporal change that can be represented may be that of 1000 samples.

Further, as a second illustration, disturbances may be more visible inplot 604 (a min max downsampled chart) due to downsampling methods. Forexample, a low pass filter may be first applied to the data prior todecimation, and such low pass filter may remove any high-frequencychanges and lessen step changes (which may be broad frequency). Iffiltering is first applied (e.g. to isolate high-frequency changes),then the largest changes may be preserved by Min Max downsampling.

FIG. 6B illustrates a plot 604 showing exemplary data with a high-passfiltered 60 Hz fundamental signal at zoom level 1%, S value of 50000,and T value of 512, in accordance with one embodiment. As an option, theplot 604 may be implemented in the context of any one or more of theembodiments set forth in any previous and/or subsequent figure(s) and/ordescription thereof. However, it is to be appreciated that the plot 604may be implemented in the context of any desired environment.

Similar to plot 600, plot 604 is shown at zoom level 1% but, incomparison to plot 600 which showed an unfiltered signal, plot 604 showsa high-pass filtered signal. Note that the disturbances 606 correspondwith high-frequency data. Each disturbance of disturbances 606 mayrepresent activity at the top and bottom of voltage waveforms.Additionally, such disturbances 606 are more readily and easilyobserved, especially compared to the unfiltered disturbance 602.

FIG. 7A illustrates a plot 704 showing exemplary data with unfiltered 60Hz fundamental signal at zoom level 0.1%, S value of 500000, and T valueof 512, in accordance with one embodiment. As an option, the plot 704may be implemented in the context of any one or more of the embodimentsset forth in any previous and/or subsequent figure(s) and/or descriptionthereof. However, it is to be appreciated that the plot 704 may beimplemented in the context of any desired environment.

As shown in plot 700, at zoom level 0.1%, a disturbance 702 may beobserved. In contrast to plot 600 which only slightly showed adisturbance in comparison to plot 604, the disturbance 702 on plot 700is more readily or easily observed in comparison to plot 704.

FIG. 7B illustrates a plot 700 showing exemplary data with a high-passfiltered 60 Hz fundamental signal at zoom level 0.1%, S value of 500000,and T value of 512, in accordance with one embodiment. As an option, theplot 700 may be implemented in the context of any one or more of theembodiments set forth in any previous and/or subsequent figure(s) and/ordescription thereof. However, it is to be appreciated that the plot 700may be implemented in the context of any desired environment.

Similar to plot 704, plot 700 is shown at zoom level 0.1% but, incomparison to plot 700 which showed a high-pass filtered signal, plot704 shows an unfiltered signal. However, whereas plot 700 showed clearlya disturbance 702, no disturbance is readily observed in plot 704.Additionally, due to the large low frequency signal in plot 704, it isnot possible to visually identify the disturbance. However, applicationof a high pass filter may remove the low frequency signal, and theremaining high-frequency signal may include a visible disturbance, asemphasized in plot 700. To this end, application of a high pass filterto the unfiltered signal (as shown in FIG. 7A) may cause low frequencydata to be removed (while allowing high-frequency data to remain),resulting in high-frequency signals containing a visible disturbance (asshown in FIG. 7B).

FIG. 8A illustrates a plot showing exemplary data with an unfiltered 60Hz fundamental signal at zoom level 0.01%, S value of 5000000, and Tvalue of 512, in accordance with one embodiment. As an option, the plot800 may be implemented in the context of any one or more of theembodiments set forth in any previous and/or subsequent figure(s) and/ordescription thereof. However, it is to be appreciated that the plot 800may be implemented in the context of any desired environment.

As shown in plot 800, at zoom level 0.01%, many disturbances 802 may beobserved. Here, the shading 804 between the maximum value and theminimum value may also be readily observed. Additionally, plot 800 showsthat the disturbance is so great, it may affect the overall voltagemagnitude. In view of such, at extreme zoom levels (e.g. such as 0.01%)the representation of the maximum value and the minimum value may beused as a magnitude estimate. For example, min max downsampling mayallow to downsample at a rate that is higher than an underlyingfundamental frequency. Further, if a fundamental wave is 60 Hz and thesample acquisition rate is 10 kHz, then approximately 167 samples per 60Hz waveform may result. If the min max downsample rate is wider than thefundamental frequency sampling period (e.g. 167 samples), then eachwaveform may be represented by a single min and single max which may bethe max and min samples peaks of the waveform. Such representation maybe an approximation of a peak of the waveform. In one embodiment, therepresentation may be further interpolated to a higher sampling rate inorder to determine a more precise peak and trough.

FIG. 8B illustrates a plot 806 showing exemplary data with a high-passfiltered 60 Hz fundamental signal at zoom level 0.01%, S value of5000000, and T value of 512, in accordance with one embodiment. As anoption, the plot 806 may be implemented in the context of any one ormore of the embodiments set forth in any previous and/or subsequentfigure(s) and/or description thereof. However, it is to be appreciatedthat the plot 806 may be implemented in the context of any desiredenvironment.

Similar to plot 800, plot 806 is shown at zoom level 0.01% but, incomparison to plot 800 which showed an unfiltered signal, plot 806 showsa high-pass filtered signal. Similar to plot 800, plot 806 showsmultiple disturbances 806 which may be readily and easily observed.Further, the shading 808 between the maximum value and the minimum valuemay also be readily observed. At such an extreme zoom level of 0.01% andwith an S value of 5000000 and a T value of 512, nearly 9766 bins wouldresult (i.e. T bins of S/T width). Such a plot emphasizes the need forusing maximum values and minimum values, else the processor would beincreasingly consumed in trying to display all raw data points. Ratherthan display all raw data points, however, through applying the methoddescribed herein, the processor can focus on displaying just the maximumvalue and the minimum value, thereby effectively reducing processor loadand demands.

FIG. 9 illustrates a method 900 for transferring information forgenerating a representation of high-frequency signal data, in accordancewith one embodiment. As an option, the method 900 may be implemented inthe context of any one or more of the embodiments set forth in anyprevious and/or subsequent figure(s) and/or description thereof. Ofcourse, however, the method 900 may be implemented in the context of anydesired environment. Further, the aforementioned definitions may equallyapply to the description below.

As shown, per operation 902, a plot of the maximum value(s) and theminimum value(s) is received. In various embodiments, the plot may bereceived by an IED, a device connected to an IED, a database, a server,and/or any device capable of receiving the plot and/or input data. Next,per operation 904, in one embodiment, a package is created of the plot,the maximum value(s), the minimum value(s), and accompanying metadata.In another embodiment, a package may not be created and the plot of themaximum value(s) and the minimum value(s) may be directly stored on afirst device and then transferred to a second device. In the context ofthe present description, the metadata may include any informationassociated with the plot, the input data, the maximum value(s), or theminimum value(s). For example, in one embodiment, the metadata mayinclude a date and time of the plot, a location (e.g. sensor device,etc.) from which the signal data originated, etc.

Additionally, per operation 906, the package is stored at a firstdevice. The first device may be an IED, a device connected to an IED(e.g. mass storage 284), a database, a server, and/or any device capableof saving the package. In one embodiment, the first device may store thepackage for a set time period, or after transferring the package to asecond device (per operation 912), the first device may delete suchpackage from the first device.

Next, per operation 908, an applicable second device is identified. Forexample, a downstream device (from the first device), a managing device,or a device associated with a technician and/or administrator, may beidentified as being applicable to the data contained within the package.Additionally, a sample of high-frequency signal data may include amaximum value and a minimum value corresponding with a detected signalanomaly. A first IED that captured such signal data may identifyaccompanying metadata such as a data, time, location, sensoridentification, magnitude of the error (e.g. priority of error ranking,etc.), etc. The first IED (or any device receiving the input data),based on the metadata captured, may determine that the error should beescalated to a managing technician and send the package to such managingtechnician for review. In this manner, the first IED receives the sampleof signal data, creates a downsampled package (e.g. to decrease dataamount, etc.), identifies where the package should be sent, and sendsthe package to the appropriate destination.

Moreover, per decision 910, it is determined whether the second deviceis ready for transfer. In one instance, the second device may be readyimmediately. In other instances, the second device may be offline for attime period or otherwise temporarily unavailable. Still yet, if thefirst device communicates with the second device via an intermediarydevice (e.g. server, relay station, etc.), the first device may relaythe package to the intermediary device to be forwarded on to the seconddevice when it is available. If the second device is not ready fortransfer, then the method loops back to decision 910. Once the seconddevice is available, then per operation 912, the package is transferredto the second device.

Additionally, the package may be analyzed at the second device peroperation 914. For example, the package may be scanned for one or moreerror flags, a detected anomaly(ies), etc. Based on the analysis, peroperation 916, an action is applied. Such action may include notifying auser (e.g. technician, etc.), saving the plot as an image (e.g. TIFF,etc.), compiling the package as a formal report to be sent to one ormore individuals, causing an effect (e.g. close, start, shut-down,verify data, reset, etc.) to occur on a power system component (e.g.sensor, generator, etc.), etc. In one embodiment, if analyzing thepackage does not identify any needed actions, the package may beconfigured to be presented to a user (e.g. technician) for review. Forexample, the plot may be displayed with the maximized value and theminimized value for presentation to an end user, wherein the end usermay manipulate the plot (e.g. zooming in and out) to more effectivelyanalyze any potential discrepancies. In such manner, the package may bemanually reviewed for accuracy.

Next, it is determined per decision 918 whether all actions are done. Ifnot, then per operation 920, the next action is applied based on theanalysis (see operation 914). Once all actions have been completed, thenthe method ends. In this manner, all actions identified by the seconddevice may be implemented.

In one embodiment, the representation of high-frequency signal data mayinclude generating a thumbnail of the plot, a preconfigured size (e.g.1024×512 pixels) image of the plot, etc. Such a thumbnail orpreconfigured size image may be sent downstream to a subsequent devicefor processing. For example, if the minimum value and maximum value (asdisplayed on the plot) were received by a second device, such seconddevice may analyze the contents, which may in turn trigger an action toanalyze the data further, send the package on to another device, etc. Inone embodiment, the second device may choose to ignore the package (i.e.no high priority disturbances were detected or did not surpass apreconfigured threshold, etc.).

In another embodiment, the package sent to a second device (or anysubsequent device) may include compressed data (e.g. the plot, theminimum value(s), the maximum value(s)). The subsequent device may firstanalyze the metadata (which may not be compressed) to determine if thecompressed package warrants to be uncompressed and further analyzed. Ifthe metadata, for example, indicates that the disturbance is of minimalsystem impact, the system may rank the package low, until a low-pointtime is found (where no other higher ranking packages need an action tobe taken) when such package can be uncompressed and analyzed. In anotherembodiment, if the package ranks below a predetermined rankingthreshold, the system may proceed to discard the package and not takeany further action.

FIG. 10A illustrates a process 1000 for identifying min and maxdownsampled values, in accordance with one embodiment. As an option, theprocess 1000 may be implemented in the context of any one or more of theembodiments set forth in any previous and/or subsequent figure(s) and/ordescription thereof. However, it is to be appreciated that the process1000 may be implemented in the context of any desired environment.

As shown, process 1000 includes input data (represented as “x(n)”) whichcan then be subsequently max downsampled 1002 or min downsampled 1004.Each max downsampling and each min downsampling can then be eachiteratively downsampled further to N iterations. As an example, a firstmax downsampling of 10× may result in a max downsampling of 10×. Asubsequent max downsampling of an additional 10× may result in a maxdownsampling of 100×, and so forth to N iterations. In like manner, afirst min downsampling of 10× may result in a min downsampling of 10×. Asubsequent min downsampling of an additional 10× may result in a mindownsampling of 100×, and so forth to N iterations. In one embodiment, apre-identification of a pre-maximum value and a pre-minimum value may beidentified for use in calculating a max/min downsampled value (throughapplying the process 1000).

In one embodiment, given a bin of N samples, a minimum value sample anda maximum value sample may represent a min and max for such bin.Additionally, a determination of a minimum value sample and maximumvalue sample may be performed for each bin range. For example, in oneembodiment, process 1000 may be used to satisfy operation 312 where, foreach bin, the maximum value x_max[k] and the minimum value x_min[k] maybe identified and/or calculated. Further, when using one or more min andmax outputs to cascade into further min and max downsampling (as shownin process 1000), an initial minimum value (or intermediately determinedminimum value) may be used as a basis for the ultimately determinedminimum value, and an initial maximum value (or intermediatelydetermined maximum value) may be used as a basis for the ultimatelydetermined maximum value.

Further, each layer of processed data (e.g. original data, 10× Maxdownsampled data, 100× Max downsampled data, etc.) may be stored in adatabase (e.g. local, remote, etc.). To this end, the stored data mayallow rapid display of both zoomed-out data (including pre-computationof zoomed out views). In this manner, recording signal data at a varietyof zoom levels may allow to quickly display associated minimum andmaximum waveforms at any arbitrary zoom level (e.g. from full originaldata to any time (years), etc.).

FIG. 10B illustrates a plot 1006 applying the process 1000, inaccordance with one embodiment. As an option, the plot 1006 may beimplemented in the context of any one or more of the embodiments setforth in any previous and/or subsequent figure(s) and/or descriptionthereof. However, it is to be appreciated that the plot 1006 may beimplemented in the context of any desired environment.

As shown, plot 1006 shows an exemplary plot of a signal, wherecompressed packet sizes are shown in bytes per packet number.

FIG. 10C illustrates a plot 1008 which is a 10× downsampling of plot1006, in accordance with one embodiment. As an option, the plot 1008 maybe implemented in the context of any one or more of the embodiments setforth in any previous and/or subsequent figure(s) and/or descriptionthereof. However, it is to be appreciated that the plot 1008 may beimplemented in the context of any desired environment.

As shown, plot 1008 is a 10× min downsampling and a 10× max downsamplingof plot 1006.

FIG. 10D illustrates a plot 1010 which is a 100× downsampling of plot1006, in accordance with one embodiment. As an option, the plot 1010 maybe implemented in the context of any one or more of the embodiments setforth in any previous and/or subsequent figure(s) and/or descriptionthereof. However, it is to be appreciated that the plot 1010 may beimplemented in the context of any desired environment.

As shown, plot 1010 is a 100× min downsampling and a 100× maxdownsampling of plot 1006.

FIG. 10E illustrates a plot 1012 of a voltage signal, in accordance withone embodiment. As an option, the plot 1012 may be implemented in thecontext of any one or more of the embodiments set forth in any previousand/or subsequent figure(s) and/or description thereof. However, it isto be appreciated that the plot 1012 may be implemented in the contextof any desired environment.

As shown, a raw voltage signal corresponds with plot 1012. As will beshown hereinafter (e.g. in FIGS. 10F-10J), plot 1012 may be downsampledin stages. Additionally, FIG. 10K is a high pass filtered version ofplot 1012. In one embodiment, min max downsampling may be applied tosignals after other filtering (e.g. including any filtering needed torepresent the signal in such a way as to facilitate analysis of specificaspects of that signal) has occurred. For example, a high pass filtermay be applied to the original signal in order to remove low frequencyinformation that may not be of interest when performing high-frequencyanalysis.

FIG. 10F illustrates a plot 1014 of a 10× min max downsampled signal ofplot 1012, in accordance with one embodiment. As an option, the plot1014 may be implemented in the context of any one or more of theembodiments set forth in any previous and/or subsequent figure(s) and/ordescription thereof. However, it is to be appreciated that the plot 1014may be implemented in the context of any desired environment.

As shown, plot 1014 is a 10× min downsampling and a 10× max downsamplingof plot 1012.

FIG. 10G illustrates a plot 1016 of a 100× min max downsampled signal ofplot 1012, in accordance with one embodiment. As an option, the plot1016 may be implemented in the context of any one or more of theembodiments set forth in any previous and/or subsequent figure(s) and/ordescription thereof. However, it is to be appreciated that the plot 1016may be implemented in the context of any desired environment.

As shown, plot 1016 is a 100× min downsampling and a 100× maxdownsampling of plot 1012.

FIG. 10H illustrates a plot 1018 of a 1000× min max downsampled signalof plot 1012, in accordance with one embodiment. As an option, the plot1018 may be implemented in the context of any one or more of theembodiments set forth in any previous and/or subsequent figure(s) and/ordescription thereof. However, it is to be appreciated that the plot 1018may be implemented in the context of any desired environment.

As shown, plot 1018 is a 1000× min downsampling and a 1000× maxdownsampling of plot 1012.

FIG. 10I illustrates a plot 1020 of a 10000× min max downsampled signalof plot 1012, in accordance with one embodiment. As an option, the plot1020 may be implemented in the context of any one or more of theembodiments set forth in any previous and/or subsequent figure(s) and/ordescription thereof. However, it is to be appreciated that the plot 1020may be implemented in the context of any desired environment.

As shown, plot 1020 is a 10000× min downsampling and a 10000× maxdownsampling of plot 1012.

FIG. 10J illustrates a plot 1022 of a 20000× min max downsampled signalof plot 1012, in accordance with one embodiment. As an option, the plot1022 may be implemented in the context of any one or more of theembodiments set forth in any previous and/or subsequent figure(s) and/ordescription thereof. However, it is to be appreciated that the plot 1022may be implemented in the context of any desired environment.

As shown, plot 1022 is a 20000× min downsampling and a 20000× maxdownsampling of plot 1012.

FIG. 10K illustrates a plot 1024 of a high pass filter applied to avoltage signal, in accordance with one embodiment. As an option, theplot 1024 may be implemented in the context of any one or more of theembodiments set forth in any previous and/or subsequent figure(s) and/ordescription thereof. However, it is to be appreciated that the plot 1024may be implemented in the context of any desired environment.

As shown, plot 1024 shows a modification of plot 1012 where a high passfilter has been applied. In one embodiment, min max filtering (as shown,e.g., in plot 1024) may allow for analysis of a voltage signal withrespect to absolute peaks of the waveforms over time when zoomed outsufficiently (which may depend on the original acquisition sample rate).For example, if sampling at 1 MHz and where a fundamental frequency isbetween 50 and 70 Hz, then the voltage signal may be downsampled by afactor of 20000× to ensure at least one waveform per downsample window.If it were desired to investigate the voltage signal at a lesserdownsampling amount, a root mean square (“RMS”) transform may be appliedto the original voltage signal. Of course, it is noted that other formsof filtering and transformations may be applied to min max downsampling.The examples therefore presented herewith are not intended to belimiting in any manner.

FIG. 10L illustrates a plot 1026 of a 10× min max downsampled signal ofplot 1024, in accordance with one embodiment. As an option, the plot1026 may be implemented in the context of any one or more of theembodiments set forth in any previous and/or subsequent figure(s) and/ordescription thereof. However, it is to be appreciated that the plot 1026may be implemented in the context of any desired environment.

As shown, plot 1026 is a 10× min downsampling and a 10× max downsamplingof plot 1024.

FIG. 10M illustrates a plot 1028 of a 100× min max downsampled signal ofplot 1024, in accordance with one embodiment. As an option, the plot1028 may be implemented in the context of any one or more of theembodiments set forth in any previous and/or subsequent figure(s) and/ordescription thereof. However, it is to be appreciated that the plot 1028may be implemented in the context of any desired environment.

As shown, plot 1028 is a 100× min downsampling and a 100× maxdownsampling of plot 1024.

FIG. 10N illustrates a plot 1030 of a 1000× min max downsampled signalof plot 1024, in accordance with one embodiment. As an option, the plot1030 may be implemented in the context of any one or more of theembodiments set forth in any previous and/or subsequent figure(s) and/ordescription thereof. However, it is to be appreciated that the plot 1030may be implemented in the context of any desired environment.

As shown, plot 1030 is a 1000× min downsampling and a 1000× maxdownsampling of plot 1024. Additionally, plot 1030 shows a min and maxvalue at each time value with a shading (e.g. shown as crisscross lines,etc.) in between each of the min and max values. In other embodiments,display of the min and max values may include at least one of: 1) twosignal waveforms, one at a minimum and one at a maximum; 2) two signalwaveforms displayed in bold, with shading between (as presently shown inplot 1030); and 3) shading between and including the minimum and maximumvalues, without bolding the signals at the actual minimum and maximum.

In various embodiments, min max downsampling may occur in stages and maybe applied to a raw voltage signal. Further, a high pass filter may beapplied to a raw voltage signal for further processing. Additionally,high pass filtering followed by min max downsampling may allow forinvestigation of very large amounts of data for excursions beyond knownthresholds.

FIG. 11 illustrates a network architecture 1100, in accordance with onepossible embodiment. As shown, at least one network 1102 is provided. Inthe context of the present network architecture 1100, the network 1102may take any form including, but not limited to a telecommunicationsnetwork, a local area network (LAN), a wireless network, a wide areanetwork (WAN) such as the Internet, peer-to-peer network, cable network,etc. While only one network is shown, it should be understood that twoor more similar or different networks 1102 may be provided.

Coupled to the network 1102 is a plurality of devices. For example, aserver computer 1112 and an end user computer 1108 may be coupled to thenetwork 1102 for communication purposes. Such end user computer 1108 mayinclude a desktop computer, lap-top computer, and/or any other type oflogic. Still yet, various other devices may be coupled to the network1102 including a personal digital assistant (PDA) device 1110, a mobilephone device 1106, a television 1104, etc.

FIG. 12 illustrates an exemplary system 1200, in accordance with oneembodiment. As an option, the system 1200 may be implemented in thecontext of any of the devices of the network architecture 1100 of FIG.11. Of course, the system 1200 may be implemented in any desiredenvironment.

As shown, a system 1200 is provided including at least one centralprocessor 1202 which is connected to a bus 1212. The system 1200 alsoincludes memory 1204 [e.g. random access memory (RAM), etc.]. The system1200 also includes a communication interface 1208 and an I/O interface1210.

The system 1200 may also include a secondary storage 1206. The secondarystorage 1206 includes, for example, a hard disk drive and/or a removablestorage drive, representing a floppy disk drive, a magnetic tape drive,a compact disk drive, etc. The removable storage drive reads from and/orwrites to a removable storage unit in a well known manner.

Computer programs, or computer control logic algorithms, may be storedin the main memory 1204, the secondary storage 1206, and/or any othermemory, for that matter. Such computer programs, when executed, enablethe system 1200 to perform various functions (as set forth above, forexample). Memory 1204, storage 1206 and/or any other storage arepossible examples of non-transitory computer-readable media.

It has been observed that under certain conditions, a power systemsignal may exhibit attributes wherein the minimum and maximum values foreach time bin (or segment) are simply the first and last values in eachtime bin. In such conditions, the Min and Max values as described above(normal Min Max) may not be as useful for the display as described. Forexample, when representing signals that contain a dominant frequency(e.g. voltage or current), and where those signals are oversampled withrespect to the dominant frequency (e.g. 1 kHz or higher when dealingwith an electric power system operating at nominally 50 Hz or 60 Hz),use of the above-described Min Max downsampling would result in normalMin Max outputs where the Min and Max represent the difference betweenthe first and last samples of the bin at the incoming frequency ratherthan the underlying signal itself. What is needed is a system andmethods to better represent the underlying signal itself, even when thesignals contain a dominant frequency, and the signals are oversampledwith respect to the dominant frequency.

Disclosed herein are systems and methods for improving therepresentation of the underlying signals, even when the signals containa dominant frequency, and the signals are oversampled with respect tothe dominant frequency. The methods and systems disclosed herein mayincorporate an underlying trend of the underlying signal to determinenew Min and Max values. As such, a better approximation of the signalmay be obtained, while still representing areas where the frequencycontent is hidden by the output signal.

FIG. 13A illustrates a plot of a magnitude of an electric power systemsignal 1302 over time, including minimum and maximum values (normal MinMax) of the electric power system signal magnitude in accordance withseveral embodiments described above. Within bin 1322, the signalincludes a local maximum 1332 and a local minimum 1336. For ease ofcomparison, also illustrated is center point 1316 at the horizontal(time) center of the bin 1322 corresponding with the magnitude of thesignal 1302 at that point. Horizontal line 1382 illustrates themagnitude of the signal 1302 at that mid-point time 1316 in bin 1322.Further, the magnitude of the maximum 1332 is illustrated at 1344 inline with the time center 1316; and the magnitude of the minimum 1336 isillustrated at 1338 in line with the time center 1316. The vertical areabetween the maximum 1332 and minimum 1338 for time bin 1332 would coverthe entire signal 1302. Accordingly, the normal Min and Max as describedin several embodiments above may be used to represent the range ofvalues within a time bin.

In an improvement to the above embodiments, trend minimum and maximumvalues may be used instead of the minimum and maximum values asdescribed above. This allows for better use and visualization of anelectric power system signal, especially when the signal contains adominant frequency and the signal is sampled at a rate greater thanNyquist with respect to the frequency. To illustrate the improvement,FIG. 13B illustrates the time bin 1322 and electrical signal 1302 ofFIG. 13A. Trendline 1382 of the electrical signal 1302 is illustrated.It should be noted that the trendline 1382 correlates with a trend ofthe electrical signal 1302 over the time of bin 1322. A trend maximumand a trend minimum are determined using the trendline. In accordancewith a first embodiment, the trend maximum and trend minimum are foundby first determining a signal maximum 1342 as the point on the signalthat is the largest positive deviation 1326 from (furthest above) thetrendline 1382; and a signal minimum 1346 as the point on the signalthat is the largest negative deviation 1328 from (furthest below) thetrendline 1382. In accordance with one embodiment, these signaldeviations from the trendline are used as the trend minimum 1346 andtrend maximum 1342 values.

In accordance with another embodiment, also illustrated in FIG. 13B, thetrend minimum and trend maximum values are determined by shifting theminimum 1346 and maximum 1342 points along a slope equal to the slope ofthe trendline 1382 to the bin time-center, in line with point 1316. Asillustrated, the trend signal minimum 1346 which is the largest negativedeviation from the trendline 1382 may be shifted along line 1317, whichhas a slope equal to the slope of the trendline 1382, to be in line withthe center time 1316, to form trend minimum 1318. Similarly, the trendsignal maximum 1342 may be shifted along line 1313, which has a slopeequal to the slope of the trendline 1382, to be in line with the centertime 1316, to form trend maximum 1314. In accordance with oneembodiment, these shifted signal deviations from the trendline are usedas the trend minimum 1318 and trend maximum 1314 values.

The trendline 1382 may be computed for each time bin 1322. In otherembodiments, the trendline 1382 may be computed across multiple timebins, or even between time bins. In one embodiment, the trendline iscomputed as a liner interpolation between a mean point of a previous binand a mean point of a current bin. In another embodiment, the trendlinemay be computed as a continuous interpolation filter where the nthprevious average and the current average provide the linearinterpolation points. A group delay adjustment may be provided. Inanother embodiment, the trendline may be computed using polynomial ornth order linear interpolation. The trendline may be computed usingother curve fitting methods.

The trend minimum and maximum may be used in place of the normal Min Maxvalues in any of the above-described embodiments. Use of the trendminimum and maximum values may improve the previous embodiments,especially where the trend of the overall frequency of the electricsignal is more prominent than deviations within particular time bins.FIG. 14A illustrates a plot of an electric power system signal 1402 overtime bins 1422, 1424, 1426, and 1428. Minimum and maximum values inaccordance with the previous embodiments are illustrated. For example,the maximum 1404 and the minimum 1412 of bin 1428 are illustrated incomparison with the mean point 1416. It should be noted that the maximum1404 of bin 1428 is the minimum point 1404 of bin 1426; the maximum ofbin 1426 is the minimum of bin 1424; and the maximum of bin 1424 is theminimum of bin 1422. Similarly, the first sample of each bin 1422-1428is the maximum value of each bin; and the last sample in each bin1422-1428 is the minimum value of each bin. Clearly, the overall trendof the signal is more prominent than any internal deviations from a meanwithin each bin. Accordingly, use and visualization of the maximum andminimum values of each bin in accordance with the previous methods isnot as useful for use or visualization of the input data as it is forvisualizing and using the overall trend.

FIG. 14B similarly illustrates the input signal 1402 over time bins1422-1428. Instead of the previous Min Max values for minimum andmaximum in each bin, the illustrated embodiment shows trend minimum andtrend maximum values. The trend minimum and maximum may be simpledeviations from the trendline such as points 1454 (trend maximum) and1472 (trend minimum). In other embodiments, the trend minimum andmaximum may be shifted along the trendline to be align with the centerpoint 1476 such as trend maximum 1456 and trend minimum 1474. In bothinstances, the trend minimum and trend maximum are improvements over theprevious embodiments for using and visualizing input data when theoverall trend of the data is more prominent than internal deviationsfrom a mean within each bin.

The trend maximum values and trend minimum values may be used in placeof the maximum values and minimum values in the above embodiments. Forexample, the identified maximum value and identified minimum valuesdescribed in conjunction with steps 312-314 of FIG. 3 may be the trendmaximum and trend minimum values. Furthermore, the trend maximum valuesand trend minimum values may be used as the maximum and minimum valuesin the method described in FIG. 9.

It is noted, however, that for input signals where the linear trend isless prominent than deviations from a mean within each bin, thepreviously-described Min Max values may be useful. That is, as the ratiobetween the output sampling rate and the Nyquist rate of the dominantfrequency increases, each three-point group of output points moreclosely resembles a linear trend. When the linear trend is prominent,the trend minimum and trend maximum values may be more useful. When thelinear trend is less prominent (the dominant frequency bleeds throughinto the output signal), the normal Min Max values may be more useful,as is clearly seen in FIGS. 13A, 13B, 14A and 14B.

In several embodiments, therefore, the systems and methods describedherein may determine whether to use the normal Min Max values or thecurrently-described trend minimum and trend maximum values. Thisdetermination may include a comparison of an output rate and a dominantfrequency of the input signal. The output rate may be a number ofsamples of data to display per cycle of the dominant frequency. Thenumber of samples may be determined by the data length and may includedownsampling as described above. As the number of samples of data todisplay per cycle of dominant frequency (also described as the outputrate) increases, the linear trend becomes more prominent, making thetrend minimum and trend maximum more useful. Similarly, as the number ofsamples of data to display per cycle of dominant frequency (alsodescribed as the output rate) decreases, the linear trend becomes lessprominent, making the normal Min Max values more useful.

In certain embodiments, the prominence of the linear trend may berelated to a factor, J, of the dominant frequency. For example, thelinear trend may be related to Nyquist, where J is 2. When the outputrate is less than (or not greater than) the factor of the dominantfrequency, then the normal Min Max values may be used, because thelinear trend is not prominent. However, when the output rate is greaterthan (or not less than) the factor of the dominant frequency, then thetrend minimum and trend maximum may be used because the linear trend isprominent.

FIG. 15 illustrates a flow chart of a method 1500 for determiningwhether to use normal Min Max or trend minimum and trend maximum values.The method 1500 starts with determining whether the input signalcontains a dominant frequency 1502. The dominant frequency may bedetermined using a past history of the input signal. The dominantfrequency may be a setting indicating a nominal dominant frequency ofthe signal. For example, many AC electric power systems operate on 50 Hzor 60 Hz nominal. Voltage, current, and other signals from such powersystem could exhibit a dominant frequency at or near the nominalfrequency. If the input signal does not include a dominant frequency in1502, then the method uses normal Min Max values 1508 as describedfurther above.

If the input signal does include a dominant frequency at 1502, then themethod proceeds to determine whether the output rate is greater than afactor J of the dominant frequency 1504. If the output rate is notgreater than the factor of the dominant frequency at 1504, then themethod proceeds to use the normal Min Max values 1508. Otherwise, if theoutput rate is greater than the factor of the dominant frequency at1504, then the method proceeds to use the trend minimum and trendmaximum values 1506 as described herein.

It is noted that the techniques described herein, in an aspect, areembodied in executable instructions stored in a computer readable mediumfor use by or in connection with an instruction execution machine,apparatus, or device, such as a computer-based or processor-containingmachine, apparatus, or device. It will be appreciated by those skilledin the art that for some embodiments, other types of computer readablemedia are included which may store data that is accessible by acomputer, such as magnetic cassettes, flash memory cards, digital videodisks, Bernoulli cartridges, random access memory (RAM), read-onlymemory (ROM), and the like.

As used here, a “computer-readable medium” includes one or more of anysuitable media for storing the executable instructions of a computerprogram such that the instruction execution machine, system, apparatus,or device may read (or fetch) the instructions from the computerreadable medium and execute the instructions for carrying out thedescribed methods. Suitable storage formats include one or more of anelectronic, magnetic, optical, and electromagnetic format. Anon-exhaustive list of conventional exemplary computer readable mediumincludes: a portable computer diskette; a RAM; a ROM; an erasableprogrammable read only memory (EPROM or flash memory); optical storagedevices, including a portable compact disc (CD), a portable digitalvideo disc (DVD), a high definition DVD (HD-DVD™), a BLU-RAY disc; andthe like.

It should be understood that the arrangement of components illustratedin the Figures described are exemplary and that other arrangements arepossible. It should also be understood that the various systemcomponents (and means) defined by the claims, described below, andillustrated in the various block diagrams represent logical componentsin some systems configured according to the subject matter disclosedherein.

For example, one or more of these system components (and means) may berealized, in whole or in part, by at least some of the componentsillustrated in the arrangements illustrated in the described Figures. Inaddition, while at least one of these components are implemented atleast partially as an electronic hardware component, and thereforeconstitutes a machine, the other components may be implemented insoftware that when included in an execution environment constitutes amachine, hardware, or a combination of software and hardware.

More particularly, at least one component defined by the claims isimplemented at least partially as an electronic hardware component, suchas an instruction execution machine (e.g., a processor-based orprocessor-containing machine) and/or as specialized circuits orcircuitry (e.g., discreet logic gates interconnected to perform aspecialized function). Other components may be implemented in software,hardware, or a combination of software and hardware. Moreover, some orall of these other components may be combined, some may be omittedaltogether, and additional components may be added while still achievingthe functionality described herein. Thus, the subject matter describedherein may be embodied in many different variations, and all suchvariations are contemplated to be within the scope of what is claimed.

In the description above, the subject matter is described with referenceto acts and symbolic representations of operations that are performed byone or more devices, unless indicated otherwise. As such, it will beunderstood that such acts and operations, which are at times referred toas being computer-executed, include the manipulation by the processor ofdata in a structured form. This manipulation transforms the data ormaintains it at locations in the memory system of the computer, whichreconfigures or otherwise alters the operation of the device in a mannerwell understood by those skilled in the art. The data is maintained atphysical locations of the memory as data structures that have particularproperties defined by the format of the data. However, while the subjectmatter is being described in the foregoing context, it is not meant tobe limiting as those of skill in the art will appreciate that various ofthe acts and operations described hereinafter may also be implemented inhardware.

To facilitate an understanding of the subject matter described herein,many aspects are described in terms of sequences of actions. At leastone of these aspects defined by the claims is performed by an electronichardware component. For example, it will be recognized that the variousactions may be performed by specialized circuits or circuitry, byprogram instructions being executed by one or more processors, or by acombination of both. The description herein of any sequence of actionsis not intended to imply that the specific order described forperforming that sequence must be followed. All methods described hereinmay be performed in any suitable order unless otherwise indicated hereinor otherwise clearly contradicted by context

The use of the terms “a” and “an” and “the” and similar referents in thecontext of describing the subject matter (particularly in the context ofthe following claims) are to be construed to cover both the singular andthe plural, unless otherwise indicated herein or clearly contradicted bycontext. Recitation of ranges of values herein are merely intended toserve as a shorthand method of referring individually to each separatevalue falling within the range, unless otherwise indicated herein, andeach separate value is incorporated into the specification as if it wereindividually recited herein. Furthermore, the foregoing description isfor the purpose of illustration only, and not for the purpose oflimitation, as the scope of protection sought is defined by the claimsas set forth hereinafter together with any equivalents thereof entitledto. The use of any and all examples, or exemplary language (e.g., “suchas”) provided herein, is intended merely to better illustrate thesubject matter and does not pose a limitation on the scope of thesubject matter unless otherwise claimed. The use of the term “based on”and other like phrases indicating a condition for bringing about aresult, both in the claims and in the written description, is notintended to foreclose any other conditions that bring about that result.No language in the specification should be construed as indicating anynon-claimed element as essential to the practice of the claimed subjectmatter.

The embodiments described herein included the one or more modes known tothe inventor for carrying out the claimed subject matter. Of course,variations of those embodiments will become apparent to those ofordinary skill in the art upon reading the foregoing description. Theinventor expects skilled artisans to employ such variations asappropriate, and the inventor intends for the claimed subject matter tobe practiced otherwise than as specifically described herein.Accordingly, this claimed subject matter includes all modifications andequivalents of the subject matter recited in the claims appended heretoas permitted by applicable law. Moreover, any combination of theabove-described elements in all possible variations thereof isencompassed unless otherwise indicated herein or otherwise clearlycontradicted by context.

What is claimed is:
 1. A device, comprising: a non-transitory memorystoring instructions; and one or more processors in communication withthe non-transitory memory, wherein the one or more processors executethe instructions to: receive input data, wherein the input data is of afirst width of input data samples over time; process the input data tomanage display of the input data, including: dividing the input datainto one or more segments; identifying a trend of the input data;identifying, from each segment of the one or more segments, a trendmaximum value of the input data within the segment; and identifying,from each segment of the one or more segments, a trend minimum value ofthe input data within the segment; transform the input data to avisualizable representation of the input data, the visualizablerepresentation of the input data including a plot of the trend maximumvalue and the trend minimum value for each segment of the one or moresegments; and, display the plot.
 2. The device of claim 1, wherein thetrend maximum value comprises a value from the input data exhibiting amaximum positive deviation from the trend.
 3. The device of claim 1,wherein the trend minimum value comprises a value from the input dataexhibiting a minimum negative deviation from the trend.
 4. The device ofclaim 1, wherein the trend comprises a slope.
 5. The device of claim 4,wherein the trend maximum value comprises a value shifted from a maximumpositive deviation of the input data from the trend along the slope ofthe trend to align with a center of the segment.
 6. The device of claim4, wherein the trend minimum value comprises a value shifted from amaximum negative deviation of the input data from the trend along theslope of the trend to align with a center of the segment.
 7. The deviceof claim 1, wherein the trend comprises a linear interpolation betweenmean points of the input data in adjacent segments.
 8. The device ofclaim 1, wherein the plot further includes a shaded region between themaximum value and the minimum value for each segment of the one or moresegments.
 9. The device of claim 8, wherein an overlay is displayed overthe plot.
 10. The device of claim 9, wherein the overlay includes one ofa median value of each segment of the one or more segments or a filteredsampled value, the overlay being displayed in a different color than acolor of the shaded region.
 11. A method, comprising: receiving, using aprocessor, input data including high-frequency signals, wherein theinput data is of a first width; processing, using the processor, theinput data to manage display of the input data, including: dividing theinput data into one or more segments over time; identifying a trend ofthe input data; identifying, from each segment of the one or moresegments, a trend maximum value of the input data within the segment;and identifying, from each segment of the one or more segments, a trendminimum value of the input data within the segment; transforming, usingthe processor, the input data to a visualizable representation of thehigh-frequency signals, the visualizable representation of thehigh-frequency signals including a plot of the trend maximum value andthe trend minimum value for each segment of the one or more segments;and displaying, using the processor, the plot.
 12. A method, comprising:receiving, using a processor, input data including high-frequencysignals, wherein the input data is of a first width; processing, usingthe processor, the input data to manage display of the input data,including: dividing the input data into one or more segments over time;determining an output rate of data from the input data for display;determining whether the input data comprises a dominant frequency;determining whether the output rate is less than a factor of thedominant frequency; when the output rate is greater than the factor ofthe dominant frequency: determining a maximum for each segment as atrend maximum; and determining a minimum for each segment as a trendminimum; when the output rate is not greater than the factor of thedominant frequency: determining a maximum for each segment as a maximuminput data value within each segment; and determining a minimum for eachsegment as a minimum input data value within each segment; transforming,using the processor, the input data to a visualizable representation ofthe high-frequency signals, the visualizable representation of thehigh-frequency signals including a plot of the maximum and the minimumfor each segment of the one or more segments; and displaying, using theprocessor, the plot.
 13. The method of claim 12, wherein when the outputrate is greater than the factor of the dominant frequency, theprocessing comprises identifying a trend of the input data.
 14. Themethod of claim 13, wherein the trend maximum comprises a value from theinput data exhibiting a maximum positive deviation from the trend; andthe trend minimum comprises a value from the input data exhibiting amaximum negative deviation from the trend.
 15. The method of claim 13,wherein the trend comprises a slope.
 16. The method of claim 15, whereinthe trend maximum comprises a value shifted from a maximum positivedeviation of the input data from the trend along the slope of the trendto align with a center of the segment.
 17. The method of claim 15,wherein the trend minimum comprises a value shifted from a maximumnegative deviation of the input data from the trend along the slope ofthe trend to align with a center of the segment.
 18. The method of claim15, wherein the trend comprises a linear interpolation between meanpoints of the input data in adjacent segments.